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Eingeladener Vortrag
- nein (69)
The ability to track electrode degradation, both spatially and temporally, is fundamental to understand performance loss during operation of lithium batteries. X-ray computed tomography can be used to follow structural and morphological changes in electrodes; however, the direct detection of electrochemical processes related to metallic lithium is difficult due to the low sensitivity to the element. In this work, 4-dimensional neutron computed tomography, which shows high contrast for lithium, is used to directly quantify the lithium diffusion process in spirally wound Li/SOCl2 primary cells. The neutron dataset enables the quantification of the lithium transport from the anode and the accumulation inside the SOCl2 cathode to be locally resolved. Complementarity between the collected neutron and X-ray computed tomographies is shown and by applying both methods in concert we have observed lithium diffusion blocking by the LiCl protection layer and identified all cell components which are difficult to distinguish using one of the methods alone.
The evaluation of measurement uncertainty is essential for testing and calibration laboratories. This paper provides a general process description for the determination of the measurement uncertainty of quantitative data and its application to the conformity assessment with examples, and how to deal with the uncertainty of qualitative (Boolean) results, e. g. when "Alarm" or "No alarm" occours in an instrument display.
Kombinierte faseroptische Messungen in Bohrlöchern helfen bei der Detektion von Kleinstleckagen
(2020)
Seit November 2019 läuft ein breit angelegtes und vom BMWi gefördertes ZIM-Kooperationsprojekt zwischen der Bundesanstalt für Materialforschung und -prüfung (BAM), der IAB'Weimar gGmbH, der GKSO GmbH & Co. Projekt KG, der DEEP.KBB GmbH sowie der Rembe Consulting PartG mbB. Ziel des Forschungsvorhabens sind sowohl die Lokalisierung als auch die Quantifizierung insbesondere sehr kleiner Leckagen in einer Bohrlochverrohrung durch die Nutzung faseroptischer Messtechnik. Durch Messdatenkopplung und den Wegfall bewegungsbedingter Nachteile einer Messsonde werden wesentlich präzisere Ergebnisse zur Bewertung der Bohrlochintegrität als üblich
erwartet.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2020)
Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
Over decades, the German Federal Institute for Materials Research and Testing (BAM) has established a sophisticated calibration laboratory for force, temperature and electrical quantities. Since more than 15 years it is accredited, currently by the national accreditation body (DAkkS), and offers its service also to external entities on a global scale. As a public provider, we are furthermore committed to research and development activities that demand measurements with highest quality and low level of uncertainties. Two R&D examples are highlighted within this contribution.
Modern air-liners and rotor blades of wind turbines are basically made of fiber reinforced plastics (FRP). Their failure heavily impairs the serviceability and the operational safety. Consequently, knowledge of the failure behavior under static and cyclic loads is of great interest to estimate the operational strength and to compare the performance of different materials. Ideally, the damage evolution under operational load is determined with in-situ non-destructive testing techniques. Here, we report on in-situ synchrotron X-ray imaging of tensile stress induced cracks in carbon fiber reinforced plastics (CFRP) due to inter fiber failure. An in-house designed compact-tensile testing machine with a load range up to 15 kN was integrated into the beam path. Since conventional radiographs do not reveal sufficient contrast to distinct cracks due to inter fiber failure and micro cracking from fiber bundles, the Diffraction Enhanced Imaging technique (DEI) is applied in order to separate primary and scattered (refracted) radiation by means of an analyzer crystal. In the laboratory, scanning X-ray refraction topography of CFRP has been applied long before but it comes along with several disadvantages: the long total measuring time hampers real time (in-situ) measurements and the required small beam size hinders end-to-end imaging. The introduced technique overcomes both drawbacks. Imaging and tensile test rig are run unsynchronized at the greatest possible frame rate (0.7 s-1 at 28.8 µm pixel size) and smallest possible strain rate (5.5∙10-4 s-1). For 0°/90° non-crimped fabrics (ncf) the first inter fiber cracks occurred at 380 MPa (strain 0.7 %). Prior to failure at about 760 MPa (strain 2.0 %) we observe the evolution of a nearly equidistant 1 mm grid of cracks running across the entire sample in the fully damaged state before total failure.
The 5G technology promises real time data transmission for industrial processes, autonomous driving, virtual and augmented reality, E-health applications and many more. The Low Temperature Co-fired Ceramics (LTCC) technology is well suited for the manufacturing of microelectronic components for such applications. Still, improvement of the technology such as further miniaturization is required. This study focuses on the characterization of inner metallization of LTCC multilayer modules, especially on the vertical interconnect access (VIA). Critical considerations for this characterization are delamination, pore clustering in and at the edge of the VIA, deformation, and stacking offset. A LTCC multilayer consisting of a glassy crystalline matrix with silver based VIAs was investigated by synchrotron x-ray tomography (CT). The aim of this study is to propose a multitude of structural characteristic values to maximize the information gained from the available dataset. Data analysis has been done with the open source software ImageJ as well as several additional plugins. The high-resolution CT data was evaluated through 2D slices for accessibility reasons. The segmentation of all 2000 slices to assess the different regions e.g. pores, silver and glass ceramic was done by a supervised machine learning algorithm. A quantitative evaluation of shape, deformation, and porosity of the VIA with respect to its dimensions is presented and the suitability of the characterization approach is assessed.